Posted on: April 28, 2021
Written by Karthik Raman, Author of An Introduction to Computational Systems Biology
Why systems biology?
Recent years have witnessed an astronomical rise in biological ‘big data’. Spurred on by the generation of omic data at different levels—genomic, transcriptomic, proteomic, and metabolomic—it is now possible to take a more holistic view of the intricate network of processes underlying every living cell. Systems biology advocates such a holistic view, cognisant of the fact that the “whole is very different from the sum of its parts”. Systems-level models of cellular networks have provided a powerful window to study, understand and manipulate these networks. Spherical cow jokes are now passé, as models are able to sufficiently accurately capture biological complexity. Systems biology marries rigorous wet-lab experimentation with in silico modelling, data analysis in a “cycle” of iterative model-guided experiments and experiment-driven model refinement. From identifying key genes that must be disrupted in a disease-causing pathogen, to predicting the spread of the COVID-19 pandemic, models play a critical role. The 2013 Nobel citation expresses that “Today the computer is just as important a tool for chemists as the test tube”. That statement is perhaps truer today of biologists, as computation and modelling have taken centre stage in the post-genomic era.
Teaching systems biology
It is easy to convince students about the primacy of a systems approach. But how does one get going? Well, as it turns out, a web browser can be quite handy today, enabling us to tinker with models and ask interesting “what-if” questions. Such questions, which seek to understand the effect of perturbations on networks, form the cornerstone of systems biology.
Systems biology can be really fun!
Vax, developed by Salathe Lab at EPFL, is a game about epidemic prevention. Though it pre-dated the present COVID-19 pandemic, epidemiology has become a mainstream topic of interest today, and Vax shows how network theory can provide insights into epidemic spreads and planning vaccinations. BooleSim is a tool that enables browser-based creation and simulation of Boolean networks. The cell cycle demo in BooleSim is quite informative, and is able to impress upon students how simple models can provide valuable insights into the functioning of complex systems. Escher FBA provides a demo of how gene knockouts affect the metabolism of E. coli, with visual feedback on how reactions are re-routed or the entire cell shuts down. Such models of metabolism are elaborated in Part II of my book. Although these tools are simplistic by themselves, they do provide a glimpse into the power of mathematical modelling and how it can generate critical insights into biological systems. These tools are more than sufficient to pique the interest of a curious student!
A course on systems biology, like the one I teach engineers from varied backgrounds at IIT Madras, does demand the knowledge of a high-level programming language such as MATLAB or Python, and, of course, an intellectual curiosity towards how mathematical modelling and computational approaches can shed light on biological phenomena. An engineering mathematics background will no doubt be advantageous, but it is not strictly necessary.
Learning by doing is always important, but more so in the case of systems biology; the fun websites mentioned above already provide a glimpse of the power of the systems approach. Yet, a student truly learns the subject only by delving deep and teaching—teaching a computer to implement the math underlying a given model. The art of modelling can only be honed by hands-on tinkering. Troubleshooting/debugging models is the central activity in systems biology, and my book places special emphasis on this, discussing common gotchas and how to tackle them at the end of nearly every chapter. Students also enjoy the project aspect of such a course, as I have heard from them rather unanimously over the last several years.
The journey into systems biology is definitely rewarding for young researchers, as they find new ways of solving complex problems, surmounting biological complexity through rigorous mathematical modelling. As they accumulate experience using various tools, algorithms and databases, it is natural for them to begin appreciating the true potential of systems-level modelling in biology.